Advancing Vulnerability Detection: An Innovative Approach to Generate Embeddings of Code Snippets
Keywords:
GNN, embeddings, integrating, underlying, exploration, potentialAbstract
This research proposes a novel approach for generating Java code embeddings using Graph Neural Networks (GNNs). It achieves this by processing a combined representation of the code structure and functionality captured in Abstract Syntax Trees (ASTs), Program Dependence Graphs (PDGs), and Control Flow Graphs (CFGs). The GNN can then leverage these rich graph representations to capture intricate relationships within the code, leading to more informative embeddings. Evaluation shows these embeddings perform well in various software engineering tasks like code similarity detection, bug localization (over 90% precision for some vulnerabilities), and code classification. Additionally, dimensionality reduction techniques effectively visualize the code snippets based on the embeddings, revealing insights into the underlying structure and relationships. This research holds significant promise for improving software development practices. By effectively capturing complex code dependencies, it paves the way for advancements in automated code analysis. The resulting robust embeddings have the potential to revolutionize practices like code review automation, early vulnerability detection, code refactoring, and code search. Furthermore, the success of this GNN-based approach opens doors for further exploration of their potential in code analysis. However, limitations include its focus on Java and the potential influence of training data on model performance. Future directions include investigating applicability to other languages, incorporating domain-specific knowledge, developing interpretable GNNs, and integrating the embeddings with existing tools for a comprehensive code analysis platform. Overall, this research offers a significant contribution by demonstrating the effectiveness of GNNs for code embedding generation, with the potential to revolutionize automated code analysis and software development practices.
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